->Bar plot using Matplotlib to represent air quality measurements of different pollutants.
->The sample data includes pollutant categories (CO, NO2, SO2, O3) and their corresponding measurement values.
->The bar plot visually displays the measurements for each pollutant category using bars of equal width.
->Axes labels and a title are added to provide context to the plot.
->This code generates a 3D surface plot using matplotlib.
->It creates a surface plot of a mathematical function in three dimensions.
->The plot is created using sample data generated with NumPy and displayed using the plot_surface function.
->The plot is labeled with x, y, and z axes and titled as "3D Surface Plot".
->The resulting plot provides a visual representation of the surface defined by the mathematical function.
The dataset consists of air quality measurements (e.g., temperature, humidity, pollution levels) recorded at different locations in a city over a period of time.
->The Matplotlib code generates a 3D scatter plot of the air quality measurements.
->It uses randomly generated sample data for the x, y, and z coordinates.
->Each point in the scatter plot represents a specific measurement, with the color representing the data value.
->This plot provides a visual understanding of the distribution of measurements in 3D space.
->The Seaborn code creates a 3D surface plot based on the air quality measurements.
->It uses sample data for the x, y, and z coordinates, where z values are computed using a mathematical function.
->The plot visualizes the surface formed by connecting the data points, allowing us to analyze patterns and variations in the measurements.
->The Plotly code generates a 3D line plot representing the air quality measurements.
->It uses sample data for the x, y, and z coordinates, where the z values are randomly generated.
->The line plot connects the points in 3D space, allowing us to observe trends and changes in the measurements over the given range.
from plotly.offline import init_notebook_mode
init_notebook_mode()
import plotly.graph_objects as go
import numpy as np
# Generate sample data
np.random.seed(0)
n = 100
x = np.linspace(0, 10, n)
y = np.random.randn(n)
z = np.random.randn(n)
# Create a 3D line plot using Plotly
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='lines')])
fig.update_layout(scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'),
title='Air Quality Measurements - 3D Line Plot')
fig.show()
->3D scatter plot using Matplotlib to visualize data points in three-dimensional space.
->Random data for x, y, and z coordinates is generated, and the scatter plot is created with markers representing the data points.
-> Axes labels and a title are added to enhance the plot.
